Modeling and optimization of a continuous electrocoagulation process using an artificial intelligence approach

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021

Autores

Graca, NS (Autor) Outra; Ana M. Ribeiro (Autor) FEUP; Rodrigues, AE (Autor) Outra

Revista

Título: Water Science and Technology: Water Supply

Vol. 22

Páginas: 643-658

ISSN: 1606-9749

Editora: IWA PUBLISHING

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00V-8ZK

DOI: 10.2166/ws.2021.249

Abstract (EN): An artificial neural network (ANN) with the topology 8-94-85-2 (input - hidden layer 1 - hidden layer 2 - output) was used to model the operation of the continuous electrocoagulation (CEC) process for the removal of fluoride from water. After the ANN training, the sum of the squared errors (MSE) and the determination coefficient (R-2) of the testing set model predictions were 0.0088 and 0.999, respectively, showing a good generalization and model's predictive capacity. The optimization of the process cost using the genetic algorithm (GA) showed that the optimal conditions are highly dependent on the feed concentration and the fluoride removal requirements. For a 5 L of water containing 10 mg/L of fluoride, the optimal conditions to reduce the fluoride concentration below the permissible limit (1.5 mg/L) are 88.3 mA of current intensity, a flow rate of 73.6 mL/min, and the use of a series monopolar (SM) electrode configuration, corresponding to a fluoride removal of 85% and an operating cost of 0.05 Euro/L.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 16